Högskolan i Skövde

With the increase in complexity of industrial systems it becomes more and more challenging to make well-grounded decisions for system design and operation. Following the concept of Virtual Factories with Knowledge-Driven Optimization (VF-KDO), this paper proposes a graph database approach to support knowledge-driven and simulation-based optimization. With the mapping of a VF-KDO ontology to a graph database, competency questions that facilitate traceability, transparency, and group decision making can be answered. This is exemplified with an industrial use case and a scenario form academic education.

Multiple levels of classification naturally occur in many domains. Several multi-level modeling approaches account for this, and a subset of them attempt to provide their users with sanity-checking mechanisms in order to guard them against conceptually ill-formed models. Historically, the respective multi-level well-formedness schemes have either been overly restrictive or too lax. Orthogonal Ontological Classification has been proposed as a foundation for sound multi-level modeling that combines the selectivity of strict schemes with the flexibility afforded by laxer schemes. In this article, we present the second iteration of a formalization of Orthogonal Ontological Classification, which we empirically validated to demonstrate some of its hitherto only postulated claims using an implementation in ConceptBase. We discuss the expressiveness of the formal language used, ConceptBase’s evaluation efficiency, and the usability of our realization based on a digital twin example model.

As digital landscapes become increasingly complex, safeguarding sensitive information and systems against cyber threats has become a paramount concern for organizations. This paper provides a comprehensive review of how enterprise architecture modeling is used in the context of cybersecurity assessment, particularly focusing on critical infrastructures. The use of enterprise architecture models for cybersecurity is motivated by the main purpose of enterprise architecture, namely to represent and manage business and IT assets and their interdependence. While enterprise architecture modeling originally served to assess Business/IT alignment, they are increasingly used to assess the cybersecurity of the enterprise. The research questions explored include the types of enterprise architecture models used for cybersecurity assessment, how security aspects are incorporated into these models, the theoretical frameworks and reference theories applied, the research methods used for evaluation, and the strengths and limitations of these models in supporting cybersecurity assessment. This review encompasses research papers published before 2024, focusing on high-quality research from peer-reviewed journals and reputable conferences, thereby providing a structured and comprehensive overview of the current state of research in this domain.

Traditional two-level modeling approaches distinguish between class- and object features. Using UML parlance, classes have attributes which require their instances to have  object slots. Multi-Level Modeling unifies classes and objects to "clabjects", and it has been suggested that attributes and slots can and should be unified to "fields" in a similar way. The notion of deep instantiation for clabjects creates the possibility of "deep fields", i.e., fields that expand on the roles of pure attributes or pure slots. In this paper, we discuss several variants of such a "deep field" notion, pointing out the semantic differences and the various resulting trade-offs. We hope our observations will help clarify the range of options for supporting clabject fields in multi-level modeling and thus aid future MLM development.

Critical infrastructure (CIs) such as power grids link a plethora of physical components from many different vendors to the software systems that control them. These systems are constantly threatened by sophisticated cyber attacks. The need to improve the cybersecurity of such CIs, through holistic system modeling and vulnerability analysis, cannot be overstated. This is challenging since a CI incorporates complex data from multiple interconnected physical and computation systems. Meanwhile, exploiting vulnerabilities in different information technology (IT) and operational technology (OT) systems leads to various cascading effects due to interconnections between systems. The paper investigates the use of a comprehensive taxonomy to model such interconnections and the implied dependencies within complex CIs, bridging the knowledge gap between IT security and OT security. The complexity of CI dependence analysis is harnessed by partitioning complicated dependencies into cyber and cyber-physical functional dependencies. These defined functional dependencies further support cascade modeling for vulnerability severity assessment and identification of critical components in a complex system. On top of the proposed taxonomy, the paper further suggests power-grid reference models that enhance the reproducibility and applicability of the proposed method. The methodology followed was design science research (DSR) to support the designing and validation of the proposed artifacts. More specifically, the structural, functional adequacy, compatibility, and coverage characteristics of the proposed artifacts are evaluated through a three-fold validation (two case studies and expert interviews). The first study uses two instantiated power-grid models extracted from existing architectures and frameworks like the IEC 62351 series. The second study involves a real-world municipal power grid.

Multiple levels of classification naturally occur in many domains. Several multi-level modeling approaches account for this and a subset of them attempt to provide their users with sanity-checking mechanisms in order to guard them against conceptually ill-formed models. Historically, the respective multi-level well-formedness schemes have either been overly restrictive or too lax. Orthogonal Ontological Classification has been proposed as a foundation that combines the selectivity of strict schemes with the flexibility afforded by laxer schemes. In this paper, we present a formalization of Orthogonal Ontological Classification, which we empirically validated to demonstrate some of its hitherto only postulated claims using an implementation in ConceptBase. We discuss both the formalization and the implementation, and report on the limitations we encountered.

The "MULTI" workshop series has set a number of multi-level modeling challenges, each designed to allow competing multi-level modeling approaches to demonstrate their capabilities and/or to tease out their limitations. The challenges therefore have been serving a three-fold purpose: First, they have allowed technologies to demonstrate their abilities. Second, they have pointed out where technologies still fall short of providing optimal modeling support. Third, they have provided a basis for comparing competing technologies, often revealing the trade-offs implied by certain design choices. The MULTI Warehouse Challenge described in this paper is the fourth installment in this series, defining a new unique set of demanding modeling challenges. 

We are pleased to welcome you to the proceedings of the 41st edition of the International Conference on Conceptual Modeling (ER 2022), which took place during October 17–20, 2022. Originally, the conference was planned to take place in the beautiful city of Hyderabad, India, but due to the uncertain COVID-19 situation it was finally held virtually. The ER conference series aims to bring together researchers and practitioners building foundations of conceptual modeling and/or applying conceptual modeling in a wide range of software engineering fields. Conceptual modeling has never been more important in this age of uncertainty. As individuals, organizations, and nations face new and unexpected challenges, software and data must be developed that can cope with and help address this new uncertainty in an ever-faster changing world. Conceptual modeling can be used to describe, understand, and cope with increasing levels of uncertainty in our world. Conference topics of interest include the theories of concepts and ontologies underlying conceptual modeling, modeling languages, methods and tools for developing and communicating conceptual models, and techniques for transforming conceptual models into effective implementations.

The MULTI 2022 Collaborative Comparison Challenge was created to promote in-depth discussion between multi-level modeling approaches. This paper presents a comparison of DeepTelos- and DMLA-based solutions in response to the challenge. We first present each approach and solution separately, and then list the similarities and differences between the two solutions, discussing their relativestrengths and weaknesses.